ReCoDID builds on existing infrastructures and partnerships to develop a sustainable model for the storage, curation, and analyses of the complex data sets collected by infectious disease (ID)-related cohorts. While ID cohorts collect both clinical-epidemiological and terabytes of OMICS data, storage and analysis of CE and high dimensional laboratory data remains separate and developing the infrastructure for housing and analyzing high dimensional laboratory data is generally not feasible for individual studies.
ReCoDID combines groundbreaking work on data sharing in public health emergencies, equitable sample sharing, and statistical methods for leveraging high-dimensional laboratory data in the context of high levels of heterogeneity and limited sample sizes with long-term investments in cloud-computing and OMICS data curation to develop and implement a new model for collaborative research in epidemic response.
The revolution in personalized medicine has fundamentally changed clinical practice in the fields of chronic disease, cancer, and rare genetic disorders. As global investment in OMICS research increases exponentially, researchers and communities need to come together to address the widening gap between high- and low-and-middle-income countries (LMIC) in terms of whose OMICS data is being shared and analyzed and in terms of who, consequently, benefits from the advances made possible by OMICS data. Existing frameworks for sharing human OMICS data developed by and for high-income populations need to be modified to address the ethical, legal, and social implications of the storage and sharing of human OMICS data from LMIC. In this project, we develop the data architecture and methods, governance, and linkages between biobanks and cloud-based, federated data repositories needed to connect data generators to the open science community to ensure that addressing IDs that disproportionately affect populations in LMIC becomes part of the personalized medicine revolution.